Data Science Interviews Exposed
作者：禅与计算机程序设计艺术
1.简介
Data Science represents a modern technological advancement enabling organizations to address intricate challenges through the analysis of vast datasets and the implementation of sophisticated analytical methodologies. Over the years, the field of Data Science has experienced significant growth, encompassing applications across diverse sectors such as business intelligence, finance, healthcare, social media analysis, and numerous other domains. Ample job opportunities exist within this rapidly expanding industry alongside internships designed for aspiring Data Scientists. These opportunities not only facilitate transitioning into full-time roles within established organizations but also provide pathways for entrepreneurs seeking skilled engineers to grow their ventures.
Finding an entry-level position in Data Science or any other technical field often requires us to tackle numerous interview questions that assess our expertise across various domains such as machine learning algorithms、statistical modeling、big data technologies、programming languages、databases etc. However、as individuals without prior experience may struggle to navigate these challenges independently due to limited knowledge about the specific concepts involved. Furthermore、candidates often lack familiarity with all the libraries and tools commonly employed in Data Science projects、which makes it particularly challenging for them to articulate their analytical findings effectively during interviews. To address this challenge、many tech companies including Google、Facebook、Microsoft、Amazon and LinkedIn have introduced online coding platforms like LeetCode、HackerRank and GeeksforGeeks that offer pre-built code templates and practical problem-solving exercises while simultaneously helping employees enhance their technical skills through hands-on practice. Despite these valuable resources though there remains a significant barrier for potential employers in accurately evaluating a candidate's proficiency in Data Science. This article seeks to shed light on the intricacies of Data Science Interviews and provide actionable steps for improving one's chances of securing an entry-level job offer.
2.Basic Concepts & Terminologies: 在深入探讨Data Science之前，请您掌握一些基础概念，这些概念将成为您学习Data Science过程中非常有用的工具。以下是一些在深入学习Data Science前必须了解的关键术语
Supervised Learning:
Supervised Learning relies on training models using annotated datasets where feature variables (input features) are explicitly known to the model. Together with the target variable (outcome label), these data points are supplied to the algorithm to establish a relationship between inputs and outputs. Supervised learning methodologies generally fall into two primary categories: Classification tasks and Regression analyses. In classification scenarios, target labels correspond to predefined groups or categories; conversely, regression problems involve predicting continuous numerical values. Prominent supervised learning techniques encompass Linear Regression for modeling linear relationships, Logistic Regression for binary classification tasks, Decision Trees for hierarchical decision-making processes, Random Forests for ensemble-based predictions, Neural Networks for complex pattern recognition, and Support Vector Machines (SVM) for robust classification based on margin maximization.
Unsupervised Learning:
Unsupervised learning relies on training models with unlabeled datasets that contain only input variables, also referred to as features. The aim is to identify patterns and connections within the dataset while grouping similar samples. Popular unsupervised learning techniques encompass K-means clustering, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA).
Reinforcement Learning:
Reinforcement learning is a method that trains agents/robots to perform tasks through interaction with their environment and receiving feedback. This process allows machines to learn and achieve objectives by taking into account sequential decision-making processes. In various fields such as gaming, autonomous vehicles, and robotics technology, reinforcement learning has proven to be highly effective. Designing reinforcement learning systems can be approached through multiple methods including model-based reinforcement learning (MBRL), model-free reinforcement learning (MFRl), deep Q-networks (DQN), policy gradient methods (PGM), etc.
Artificial Intelligence (AI):
Artificial Intelligence is characterized by a machine's ability to simulate human cognitive abilities and make decisions through logical reasoning and problem-solving. These fields encompass the development of systems that enable machines to learn from data. Machine learning, natural language processing, speech recognition, vision recognition, and robotics are specialized domains within AI focused on creating technologies that mimic human-like functionalities.
Big Data:
Big Data refers to vast quantities of data produced in different formats—structured、semi-structured、and unstructured. Today businesses encounter significant challenges in analyzing、processing、and extracting meaningful insights from exponentially increasing amounts of diverse data. To manage Big Data effectively、organizations must employ distributed computing models such as Hadoop、Spark、and Storm for managing massive datasets alongside leveraging cloud-based solutions such as AWS、Azure、and Google Cloud Platform.
Data Engineering:
The aim of Data Engineering is to convert raw data into actionable insights through cleaning and analysis. Completing this task requires collaboration among various teams handling different segments within a data pipeline. These common roles encompass individuals such as Data Analysts、Data Engineers、Data Scientists and Data Architects. In terms of tools employed in this field、commonly used ones include SQL、Python、Apache Airflow、MongoDB Elasticsearch Kafka as well as Docker.
Database Management System (DBMS):
A Database Management System (DBMS) refers to software designed to manage databases. It enables users to create databases from scratch or integrate them with existing systems. A Database Management System facilitates the creation of databases from scratch or integration with existing systems. Popular DBMS products include MySQL, PostgreSQL, Oracle, SQLite, MariaDB,and Microsoft SQL Server.
Probabilistic Graphical Models (PGMs):
概率图模型通过概率分布来描述随机变量之间的不确定性关系。基于概率图模型的推断过程允许我们估计给定观测条件下最可能的状态组合，在数据融合到视觉追踪等多个领域中展现出广泛的应用。一些常用的PGM算法包括贝叶斯网络、马尔可夫随机场以及隐藏马尔可夫模型。
Cloud Computing Platform:
Cloud computing provides a versatile approach to allocating infrastructure resources as needed, cutting costs and fostering scalability. Prominent cloud computing platforms such as AWS (AMAZON), Microsoft Azure (MICROSOFT AZURE), and Google Cloud Platform (GCP) are widely used.
These encompass several key terms and concepts you might encounter while conducting a Data Science interview. It is advisable for you to become acquainted with these foundational elements, as this will ensure you do not overlook crucial information during your interviews. Additionally, through this process, you will develop an enhanced comprehension of each concept and its role in data science-related projects.
3.Core Algorithms and Techniques:
Having gone through the fundamentals, it is time to explore essential algorithms and methodologies that are pivotal to Data Science roles. The same as before, Data Science integrates a variety of disciplines including machine learning, statistics, mathematics, programming, databases and optimization. The following sections will delve into each subject comprehensively:
3.1 Statistical Techniques
Statistical techniques hold a pivotal position in Data Science as they enable us to clean, prepare, and analyze data. Frequentist and Bayesian methods are commonly employed in Data Science to maintain appropriate levels of error control and quantify uncertainty. We concentrate on elucidating the mathematical underpinnings of frequentist and Bayesian methodologies.
3.1.1 Frequentist Approach:
Frequentist statistics represents a key area of statistical analysis, focusing primarily on frequency analysis and hypothesis testing. It posits that observed data conforms to a normal distribution, offering p-values to evaluate hypotheses. The sequential process underlying frequentist methodology is outlined below:
- Formulate null and alternative hypotheses
- Obtain data
- Calculate required sample size for achieving desired confidence level
- Compute measures of spread and central tendency for underlying population
- Determine test statistic (Z or T score) using formula z = (x - μ)/σ
- Determine critical value based on computed Z-score from Standard Normal Distribution Table
- Compare calculated Z-score against critical value to evaluate significance of result, then decide whether to reject or fail to reject alternative hypothesis.
It indicates the likelihood of achieving outcomes at least as extreme as those observed, assuming that the null hypothesis holds true. When a p-value is below a predetermined significance threshold α, failing to reject the null hypothesis while upholding the alternative hypothesis is appropriate. In such cases, it becomes necessary to reject the null hypothesis.
3.1.2 Bayesian Approach:
贝叶斯统计学是一种概率框架，在观察到新证据时更新事件发生的可能性，并依赖于先验概率。它将每个参数视为随机变量，并在观察到新证据后计算后验概率。贝叶斯推断涉及四个主要步骤：
- 构建模型
- 更新先验信念
- 计算后验分布
- 基于观测数据得出结论
Prior Knowledge: Based on our previous experiences and prior assumptions, we posit certain key insights about the parameters under investigation.
Likelihood Function: This mathematical function quantifies the degree of plausibility associated with observing specific data given a particular hypothesis.
Posterior Distribution: This serves as an updated version of our initial assumptions (prior distribution) after incorporating new evidence.
Sampling / Optimization: Once we have a sufficiently accurate approximation of the posterior distribution, we can employ optimization techniques such as maximum likelihood estimation to generate samples or optimize parameters.
The last stage involves identifying a region of practical equivalence, which is defined by the new hypothesis generating outcomes akin to those observed under established assumptions. For instance, when altering one's perspective to accept the validity of the new hypothesis without sufficient evidence supporting its departure from current beliefs, it would be inappropriate to make such a change without thorough justification.
Mathematics is indispensable to Data Science since it equips us with essential tools to address real-world challenges. Typically, we utilize mathematical techniques including linear algebra, calculus, optimization methods, and probability theory for developing statistical models, building predictive models, and assessing performance metrics. These mathematical approaches enable the creation of statistical models for analysis and forecasting; the construction of predictive models for decision-making; and the evaluation of model performance through various metrics. The key topics often include matrix decompositions for data simplification; distance measures for similarity assessment; clustering techniques for grouping data points; and neural network architectures for complex pattern recognition.
3.2.1 线性代数 线性代数是处理向量空间和矩阵的数学分支学科，在机器学习算法中无处不在地发挥作用，尤其是涉及距离、角度和投影的那些算法。数据科学中常用的线性代数算法包括奇异值分解(Singular Value Decomposition)、主成分分析(Principal Component Analysis)、Cholesky 分解、特征分解(Eigendecomposition)以及QR 分解(QR Factorization)等方法。
3.2.2 Calculus Calculus serves as a vital branch of Data Science, playing a key role in solving mathematical equations, calculating integrals, and determining derivatives. Calculating the gradient of a loss function with respect to neural network weights is a common practice in Data Science. Various operations within calculus involve computing derivatives and integrals.
3.2.3 概率论
Probability theory is a field in mathematics that concerns itself with events and their possible outcomes.
It is ubiquitous across Data Science, enabling the description and reasoning about uncertainty and risk.
Our primary focus lies in examining two categories of probability distributions: discrete and continuous.
Discrete distributions characterize a countable number of potential outcomes,
whereas continuous distributions characterize an uncountable infinity of potential outcomes.
The integration requirement distinguishes continuous distributions from discrete ones,
which do not require integration.
3.2.4 优化
优化代表从一组可能的解决方案中选择最佳方案以解决一个问题的过程。
在数据科学领域中，优化技术广泛应用于寻找超参数的最佳值（如学习率、正则化强度和特征缩放因子）。
数据科学中使用的优化方法包括梯度下降法、牛顿法、共轭梯度法和拟牛顿法等；每种方法都有其特定的应用场景优势
在数据科学项目中扮演着关键角色的编程语言和库为我们的工作提供了强大的技术支持。它们为我们提供了编写高效且易于维护代码的工具。其中最常用的语言包括R、Python、Java、Scala、Julia、C++以及MATLAB等程序设计语言。而像Numpy、Pandas、TensorFlow、Keras、scikit-learn以及PyTorch等库则是数据分析与建模的关键组件。这些库分别提供了数据处理、可视化分析以及机器学习模型构建等功能。
Database management systems hold a pivotal position in the handling of large volumes of data. These systems not only facilitate the organization and storage of information but also provide robust mechanisms for retrieval and manipulation of data. Among various database management systems available today are MySQL PostgreSQL Oracle SQLite MariaDB as well as Microsoft SQL Server each offering unique capabilities tailored to different needs. When it comes to database operations several common tasks include constructing database schemas creating indexes performing queries and updating data efficiently.
3.5 自然语言处理（NLP） 自然语言处理（NLP），作为人工智能的一个专门领域，在计算机科学的帮助下分析、解读和处理人类语言。它主要涉及将文本数据转换为适合计算机处理的数值表示形式。在数据科学中应用这些工具是为了自动化执行任务如情感分析、实体识别以及文档分类等工作流程。一些典型的自然语言处理技术包括词干提取、词缀还原、词袋模型以及词嵌入方法。
3.6 Computer Vision Computer vision represents a field that relies on digital image processing methods to identify and locate objects, faces, and scenes. It is extensively utilized in mobile devices, security systems, autonomous vehicles, and biometric systems for practical implementations. OpenCV、Dlib、以及Tensorflow等 are prominent computer vision frameworks within the data science domain. Among other tasks, common computer vision applications encompass object detection、image segmentation、and facial recognition.
3.7 Reinforcement Learning Reinforcement Learning represents a specialized field within machine learning that draws inspiration from behavioral psychology and animal learning behaviors. Within this framework, agents are trained to optimize outcomes by maximizing a defined reward signal. Its practical applications span diverse domains such as robotics, gaming, and financial trading. Our study delves into two primary branches: model-based reinforcement learning (MBRL) and model-free reinforcement learning (MFRL).
3.7.1 Model-Based Reinforcement Learning Model-Based reinforcement learning constructs a probabilistic representation of the environment and updates this representation dynamically as the agent interacts with it. This model encapsulates the system's state by capturing its dynamics、constraints、and reward structures. Once learned, this model enables planning ahead and action selection based on anticipated outcomes. A widely recognized approach for modeling environments in reinforcement learning contexts is Partially Observable Markov Decision Processes (POMDPs), which formalizes decision-making under uncertainty by representing states through state space、action space、and observation space.
3.7.2 Model-free RL Model-free reinforcement learning operates without constructing an environmental model, instead directly exploring all possible actions. Rather than attempting to create an idealized model, it learns through trial and error by examining the experiences it accumulates. Q-learning is a widely-used model-free reinforcement learning technique that maintains and updates a table of action-state pairs iteratively. Deep Q-Learning is another prominent model-free reinforcement learning approach that leverages deep learning methods to train agents capable of solving intricate environments.
3.4 Distributed Computing Frameworks
Distributed computing frameworks enable scalable handling of computationally intensive tasks across multiple nodes. They are significantly utilized in data-driven domains due to their scalability and fault tolerance features. Popular distributed computing frameworks are known for their prominent usage in various applications such as Hadoop, Spark, and Storm. Some typical activities involve file sharing, resource scheduling, and workload partitioning.
3.5 时间序列分析
时间序列分析涉及从时间依赖性数据中提取有价值的信息。它在金融、经济、能源、电信和医疗等多个行业中得到广泛应用。时间序列分析中常用的一种类型是预测，在这种情况下我们试图根据过去的趋势来预测未来的发展方向。为了实现这一目标我们可以采用多种技术手段包括移动平均法自回归积分移动平均法和支持向量回归方法。
3.6 MapReduce, Streaming MapReduce, and streaming are prominent distributed computing models employed in Data Science. These approaches are designed to handle large-scale datasets that exceed memory capacity. MapReduce processes data using key-value pairs and performs computations across partitioned segments. In contrast, Streaming MapReduce and streaming techniques treat the continuous flow of data as a series of events, enabling incremental analysis and processing.
3.7 可扩展性和容错性 可扩展性和容错性是分布式计算框架在数据科学领域中的核心特征。当我们将其计算分布在多个节点上执行时，在高负载情况下仍需确保系统能够保持响应和可靠性。为此需要具备鲁棒的通信协议 自动恢复机制以及透明的负载均衡策略。
Big data technologies are employed in the field of Data Science to manage petabytes of information. We utilize a variety of technologies such as Hadoop, Spark, and NoSQL databases to store, process, and analyze large volumes of data. Some widely used big data technologies include The Hadoop Distributed File System (HDFS), Apache Drill technology, Apache Phoenix technology, Apache Drill technology, Apache Impala technology, Apache Kafka technology, Apache Cassandra technology, and Apache Solr technology.
3.9 其他重要技巧 我们已经列出了Data Science中未涉及的一些常用技术。此外还有其他几种关键的技术包括推荐引擎、深度学习、流数据分析以及数据可视化。